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hopfield.py
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hopfield.py
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"""hopfield.py: Hopfield Network with two training methods, namely Little Model and Sequential Update."""
__author__ = "Majd Jamal"
import numpy as np
import matplotlib.pyplot as plt
class Hopfield:
"""
Hopfield Network with Little Model and Sequential Update.
"""
def __init__(self):
self.W = None #Weights, which is the memory.
def sign(self, X):
""" Activation node
:param X: Predictions
:return: Output: The sign of each prediction.
"""
return np.where(X >= 0, 1, -1)
def fit(self, X):
""" Train the memory
:param X: Patterns. Data points as numpy arrays.
"""
self.W = X.T @ X
print(self.W.shape)
def predict(self, X):
""" Predict with Batch mode
:param X: data point.
:return: Re-created data point.
"""
return self.sign(self.W@X.T).T
def sequential_predict(self, X, steps):
""" Predict with Sequential Update
:param X: data point.
:param steps: number of iterations
:return updatedX: Re-created data point.
"""
updatedX = X
Ndim = len(X[0])
for step in range(steps):
randomInt = np.random.randint(0, Ndim)
var = updatedX @ self.W[randomInt]
var = self.sign(var)
updatedX[0][randomInt] = var
return updatedX